Popular GitHub Repositories for Minibatch Sgd

LSTM and QRNN Language Model Toolkit for PyTorch

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch. Find explanation at tourdeml.github.io/blog/

Deep learning library in plain Numpy.

APOLLO: SGD-like Memory, AdamW-level Performance

Machine learning algorithms in Dart programming language

Ternary Gradients to Reduce Communication in Distributed Deep Learning (TensorFlow)

A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.

Riemannian stochastic optimization algorithms: Version 1.0.3

Distributed Learning by Pair-Wise Averaging

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

Implementation of key concepts of neuralnetwork via numpy

Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enforces privacy by clipping and sanitising the gradients with Gaussian noise during training.

A collection of various gradient descent algorithms implemented in Python from scratch

Notes & Code to go over "Grokking Deep Learning" Book by Andrew Trask

vector quantization for stochastic gradient descent.

Code for "Adaptive Gradient Quantization for Data-Parallel SGD", published in NeurIPS 2020.

A compressed adaptive optimizer for training large-scale deep learning models using PyTorch

Computer Vision and Image Processing algorithms implemented using OpenCV, NumPy and MatPlotLib, for UOM's EN2550 Fundamentals of Image Processing and Machine Vision Module ❄

Contains code for a voting classifier that is part of an ensemble learning model for tweet classification (which includes an LSTM, a bayesian model and a proximity model) and a system for weighted voting